The lack of research combining image translation, DL and RS data in a domain adaptation setting was also pointed out in Ao et al. [1]}, where a framework for translation between different SAR sensors was proposed. Conditioned on urban inputs images from the low-resolution Sentinel-1 sensor, their dialectical GAN generates corresponding high-resolution TerraSAR-X images. The dialectical GAN uses a modification of the Pix2pix cGAN proposed in [2]} and combines concepts of both the cGAN and traditional neural network. Additional research on RS data simulation through image translation is found in [3]}. Here, three non-conditional GAN networks are considered to simulate SAR data of vehicles from random noise. Neither [1]} or [3]} specifically focuses on using image translation through GANs for regression purposes or translating between instruments with different physical measurement principles, as we intend to. In general, most of today's research on semi-supervised learning through GANs focuses on solving a classification task, and only a minority of them are tailored to regression tasks [6]}. This probably explains the lack of research within the field of RS on image translation and domain adaptation for regression purposes. Within the GAN framework, Rezagholizadeh and Haidar presented one of the first architectures aimed at regression, named the Reg-GAN [6]}. They use two different networks, where one learns data generation while the other predicts continuous labels. It is applied in a computer vision task for self-driving vehicles, where the GAN generates images of a road segment and a regression network predicts the matching steering angle. Olmschenk et al. [8]} later proposed the feature contrasting loss function and outperformed [6]} on the same semi-supervised GAN regression task. Additional examples were also shown in [8]} on the combined task of face generation and age prediction as well as on crowd counting. The proposed work in our paper differs from earlier related research [6]}, [8]}, as we do not perform any additional regression on the image content of the generated synthetic patches. This is possible due to the nature of our proposed modelling strategy with two sequential regression models, where the first regression model provides the response variables or regression target for the second model, which results in a generative model used to make predictions in new unseen areas through the image translation.
